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About This Role
Capgemini Government Solutions (CGS) LLC is seeking a highly motivated Machine Learning (ML) Engineer with an active security clearance to deliver high-quality code components to power the services, servers, distributed systems, and backend architecture. The successful candidate will have the opportunity to conceive and deliver innovative ML solutions to the U.S. Public Sector to help address critical national and global challenges. This individual will join our Data and AI practice in the DC Metro Area. As a part of the rapidly expanding CGS Data and AI practice, the candidate will also help our clients develop, deploy, and modernize their data estates and pipelines.
At Capgemini, we are committed to our staff’s professional development and offer a wide range of training and educational resources. In addition to our internal learning sites, we partnered with Coursera and Degreed to offer our staff the latest courses from academic institutions around the world. We provide education expense reimbursements as well as sponsor seminars, conferences, and certifications. Our practice leaders work with every team member to chart appropriate career paths and goals to ensure that we all stay innovative and transformative, which maximizes our ability to scale up our solutions, keep up with the cutting edge, and bring the art of what’s possible to the Federal Government.
Job Responsibilities
As a ML Engineer you will:
- Deliver high-quality code components that will power the services, servers, distributed systems, and backend architecture for Microsoft products
- Partner with industry-leading Engineers, Artists, Producers and Designers
- Incorporate the latest AI, Machine Learning and Computer vision capabilities into the design of Microsoft products and services
- Drive ML-related solutions based on evaluation of requirements, resources, and alternatives
- Conduct exploratory data analysis to evaluate data pipelines and construct data stores (structured, semi-structured, and unstructured) as needed to feed frameworks/models
- Develop custom algorithms, frameworks, and models or leverage available tools, libraries, and applications to solve complex problems
- Deploy ML solutions and develop methodologies to scale up
- Present and articulate findings and present solutions to clients and team members
- Maintain knowledge of advances of ML in industry and academia
- As needed, collaborate with internal and external stakeholders to identify object detection, optical character recognition, automation, predictive modeling, pattern analysis, natural language processing, fraud detection, and other business cases for using ML
Required Qualifications
- U.S. Citizenship and an active secret level security clearance or higher is required
- Ability to be at client site full time in Washington, DC
- Bachelor’s degree or higher in machine learning, data science, statistics, computer science, economics, mathematics, information systems, or similar field preferred
- Minimum of two (2) years of professional experience with machine learning-delivery responsibilities such as:
- + Deliver high-quality code components to power services, servers, distributed systems, and backend architecture + Incorporate the AI, machine learning, and Computer vision capabilities into solutions and services
- Experience in object detection and computer vision models such as YOLO, MMDetection, R-CNN, SSD, FPN, and RetinaNet
- Experience programming in languages such as Python, R, Scala, SQL, JavaScript, C/C++, and Java
- Experience using libraries and frameworks such as TensorFlow, PyTorch, Spark ML/MLlib, and Jupyter
- Excellent verbal and written communication skills
- Ability to multi-task and stay flexible in a dynamic work environment
Nice to have skills/qualifications
- Active TS clearance
- Data Science, ML, AI, or Cloud certifications
- Experience working in an IT project team following SDLC and DevOps methodologies
- Experience working with big data distributed programming languages and ecosystems such as Hadoop, MapReduce, Pig, or Kafka
- Experience with designing and building cloud-based databases, data lakes, and data warehouses
- Experience using tools such as Azure’s Machine Learning and Cognitive Services; AWS’s SageMaker, Polly and Rekognition; DataRobot; or H2O.ai
About Capgemini
Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem. The Group reported 2024 global revenues of €22.1 billion.
Get The Future You Want | www.capgemini.com
Disclaimer
All qualified applicants will be considered for employment based on their skills, and merit.
Please be aware that Capgemini may capture your image (video or screenshot) during the interview process and that image may be used for verification, including during the hiring and onboarding process.
Applicants for employment in the US must have valid work authorization that does not now and/or will not in the future require sponsorship of a visa for employment authorization in the US by Capgemini.
Capgemini discloses salary range information in compliance with state and local pay transparency obligations. The disclosed range represents the lowest to highest salary we, in good faith, believe we would pay for this role at the time of this posting, although we may ultimately pay more or less than the disclosed range, and the range may be modified in the future. The disclosed range takes into account the wide range of factors that are considered in making compensation decisions including, but not limited to, geographic location, relevant education, qualifications, certifications, experience, skills, seniority, performance, sales or revenue-based metrics, and business or organizational needs. At Capgemini, it is not typical for an individual to be hired at or near the top of the range for their role. The base salary range for the tagged location is $120k - $180k.
This role may be eligible for other compensation including variable compensation, bonus, or commission. Full time regular employees are eligible for paid time off, medical/dental/vision insurance, 401(k), and any other benefits to eligible employees.
Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, or any other form of compensation that are allocable to a particular employee remains in the Company's sole discretion unless and until paid and may be modified at the Company’s sole discretion, consistent with the law.
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
The contractor will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information. 41 CFR 60-1.35(c)
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Salary Context
This $120K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Capgemini Government Solutions, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($150K) sits 29% below the category median. Disclosed range: $120K to $180K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Capgemini Government Solutions AI Hiring
Capgemini Government Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $180K - $180K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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